This work introduces a dataset for large-scale instance-level recognition in
the domain of artworks. The proposed benchmark exhibits a number of different
challenges such as large inter-class similarity, long tail distribution, and
many classes. We rely on the open access collection of The Met museum to form a
large training set of about 224k classes, where each class corresponds to a
museum exhibit with photos taken under studio conditions. Testing is primarily
performed on photos taken by museum guests depicting exhibits, which introduces
a distribution shift between training and testing. Testing is additionally
performed on a set of images not related to Met exhibits making the task
resemble an out-of-distribution detection problem. The proposed benchmark
follows the paradigm of other recent datasets for instance-level recognition on
different domains to encourage research on domain independent approaches. A
number of suitable approaches are evaluated to offer a testbed for future
comparisons. Self-supervised and supervised contrastive learning are
effectively combined to train the backbone which is used for non-parametric
classification that is shown as a promising direction. Dataset webpage:
this http URL

该研究介绍了一个艺术品领域的大规模实例级别识别数据集，包含了许多具有挑战性的问题，同时结合了自监督和监督对比学习来训练骨干网络进行非参数分类，该数据集可用于鼓励探索不同领域上的领域独立方法。